2014 Imageclef Webupv Collection

Name: 2014 Imageclef Webupv Collection
Creator: Zenodo
Published: 2017-01-25T17:52:14+08:00

Zenodo2020-09-18 更新2026-05-25 收录

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资源简介：

This document describes the WEBUPV dataset compiled for the ImageCLEF 2014 Scalable Concept Image Annotation challenge. The data mentioned here indicates what is ready for download. However, upon request or depending on feedback from the participants, additional data may be released. The following is the directory structure of the collection, and bellow there is a brief description of what each compressed file contains. The corresponding MD5 checksums of the files shown (for verifying a correct download) can be found in md5sums.txt. Directory structure ------------------- . | |--- README.txt |--- md5sums.txt |--- webupv14_train_lists.zip |--- webupv14_train2_lists.zip |--- webupv14_devel_lists.zip |--- webupv14_test_lists.zip |--- webupv14_baseline.zip | |--- feats_textual/ | | | |--- webupv14_{train|train2}_textual_pages.zip | |--- webupv14_{train|train2}_textual.scofeat.gz | |--- webupv14_{train|train2}_textual.keywords.gz | |--- feats_visual/ | |--- webupv14_{train|train2|devel|test}_visual_images.zip |--- webupv14_{train|train2|devel|test}_visual_gist2.feat.gz |--- webupv14_{train|train2|devel|test}_visual_sift_1000.feat.gz |--- webupv14_{train|train2|devel|test}_visual_csift_1000.feat.gz |--- webupv14_{train|train2|devel|test}_visual_rgbsift_1000.feat.gz |--- webupv14_{train|train2|devel|test}_visual_opponentsift_1000.feat.gz |--- webupv14_{train|train2|devel|test}_visual_colorhist.feat.gz |--- webupv14_{train|train2|devel|test}_visual_getlf.feat.gz Contents of files ----------------- * webupv14_train{|2}_lists.zip The first training set ("train_*") includes images for the concepts of the development set, whereas the second training set ("train2_*") includes images for the concepts in the test set that are not in the development set. -> train{|2}_iids.txt : IDs of the images (IIDs) in the training set. -> train{|2}_rids.txt : IDs of the webpages (RIDs) in the training set. -> train{|2}_*urls.txt : The original URLs from where the images (iurls) and the webpages (rurls) were downloaded. Each line in the file corresponds to an image, starting with the IID and is followed by one or more URLs. -> train{|2}_rimgsrc.txt : The URLs of the images as referenced in each of the webpages. Each line of the file is of the form: IID RID URL1 [URL2 ...]. This information is necessary to locate the images within the webpages and it can also be useful as a textual feature. * webupv14_{devel|test}_lists.zip -> {devel|test}_conceptlists.txt : Lists per image of concepts for annotation. Each line starts with an image ID and is followed by the list of concepts in alphabetical order. Each ID may appear more than once. In total there are 1940 image annotation lists for the development set and 7291 image annotation lists for the test set. These correspond to 1000 and 4122 unique images (IDs) for the development and test sets, respectively. -> {devel|test}_allconcepts.txt : Complete list of concepts for the development/test set. The concepts are defined by one or more WordNet synsets, which is intended to make it possible to easily obtain more information about the concepts, e.g. synonyms. In the concept list, the first column (which is the name of the concept) indicates the word to search in WordNet, the second column the synset type (either noun or adjective), the third column is the sense number and the fourth column is the WordNet offset (although this cannot be trusted since it changes between WordNet versions). For most of the concepts there is a fifth column which is a Wikipedia article related to the concept. -> {devel|test}_groundtruth.txt : Ground truth concepts for the development and test sets. -> {devel|test}_*urls.txt : The original URLs from where the images (iurls) and the webpages (rurls) were downloaded. Each line in the file corresponds to an image, starting with the IID and is followed by one or more URLs. Note: These are included only to acknowledge the source of the data, not be used as input to the annotation systems. * webupv14_baseline.zip An archive that includes code for computing the evaluation measures for two baseline techniques. See the included README.txt for details. * feats_textual/webupv14_train{|2}_textual_pages.zip Contains all of the webpages which referenced the images in the training set after being converted to valid xml. In total there are 262588 files, since each image can appear in more than one page, and there can be several versions of same page which differ by the method of conversion to xml. To avoid having too many files in a single directory (which is an issue for some types of partitions), the files are found in subdirectories named using the first two characters of the RID, thus the paths of the files after extraction are of the form: ./WEBUPV/pages/{RID:0:2}/{RID}.{CONVM}.xml.gz To be able to locate the training images withing the webpages, the URLs of the images as referenced are provided in the file train_rimgsrc.txt. * feats_textual/webupv14_train{|2}_textual.scofeat.gz The processed text extracted from the webpages near where the images appeared. Each line corresponds to one image, having the same order as the train_iids.txt list. The lines start with the image ID, followed by the number of extracted unique words and the corresponding word-score pairs. The scores were derived taking into account 1) the term frequency (TF), 2) the document object model (DOM) attributes, and 3) the word distance to the image. The scores are all integers and for each image the sum of scores is always <=100000 (i.e. it is normalized). * feats_textual/webupv14_train{|2}_textual.keywords.gz The words used to find the images when querying image search engines. Each line corresponds to an image (in the same order as in train_iids.txt). The lines are composed of triplets: [keyword] [rank] [search_engine] where [keyword] is the word used to find the image, [rank] is the position given to the image in the query, and [search_engine] is a single character indicating in which search engine it was found ('g':google, 'b':bing, 'y':yahoo). * feats_visual/webupv14_*_images.zip Contains thumbnails (maximum 640 pixels of either width or height) of the images in jpeg format. To avoid having too many files in a single directory (which is an issue for some types of partitions), the files are found in subdirectories named using the first two characters of the IID, thus the paths of the files after extraction are of the form: ./WEBUPV/images/{IID:0:2}/{IID}.jpg * feats_visual/webupv14_*.feat.gz The visual features in a simple ASCII text sparse format. The first line of the file indicates the number of vectors (N) and the dimensionality (DIMS). Then each line corresponds to one vector, starting with the number of non-zero elements and followed by pairs of dimension-value, being the first dimension 0. In summary the file format is: N DIMS nz1 Dim(1,1) Val(1,1) ... Dim(1,nz1) Val(1,nz1) nz2 Dim(2,1) Val(2,1) ... Dim(2,nz2) Val(2,nz2) ... nzN Dim(N,1) Val(N,1) ... Dim(N,nzN) Val(N,nzN) The order of the features is the same as in the lists devel_conceptlists.txt, test_conceptlists.txt and train_iids.txt. The procedure to extract the SIFT based features in this subdirectory was conducted as follows. Using the ImageMagick software, the images were first rescaled to having a maximum of 240 pixels, of both width and height, while preserving the original aspect ratio, employing the command: convert {IMGIN}.jpg -resize '240>x240>' {IMGOUT}.jpg Then the SIFT features where extracted using the ColorDescriptor software from Koen van de Sande (http://koen.me/research/colordescriptors). As configuration we used, 'densesampling' detector with default parameters, and a hard assignment codebook using a spatial pyramid as 'pyramid-1x1-2x2'. The number in the file name indicates the size of the codebook. All of the vectors of the spatial pyramid are given in the same line, thus keeping only the first 1/5th of the dimensions would be like not using the spatial pyramid. The codebook was generated using 1.25 million randomly selected features and the k-means algorithm. The GIST features were extracted using the LabelMe Toolbox. The images where first resized to 256x256 ignoring original aspect ratio, using 5 scales, 6 orientations and 4 blocks. The other features colorhist and getlf, are both color histogram based extracted using our own implementation.

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Zenodo

创建时间：

2017-01-25

2014 Imageclef Webupv Collection

资源简介：

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